Target labelling for the detection and profiling of microRNAs expressed in CNS tissue using microarrays.
Bibliographic record
Abstract
BACKGROUND: MicroRNAs (miRNA) are a novel class of small, non-coding, gene regulatory RNA molecules that have diverse roles in a variety of eukaryotic biological processes. High-throughput detection and differential expression analysis of these molecules, by microarray technology, may contribute to a greater understanding of the many biological events regulated by these molecules. In this investigation we compared two different methodologies for the preparation of labelled miRNAs from mouse CNS tissue for microarray analysis. Labelled miRNAs were prepared either by a procedure involving linear amplification of miRNAs (labelled-aRNA) or using a direct labelling strategy (labelled-cDNA) and analysed using a custom miRNA microarray platform. Our aim was to develop a rapid, sensitive methodology to profile miRNAs that could be adapted for use on limited amounts of tissue. RESULTS: We demonstrate the detection of an equivalent set of miRNAs from mouse CNS tissues using both amplified and non-amplified labelled miRNAs. Validation of the expression of these miRNAs in the CNS by multiplex real-time PCR confirmed the reliability of our microarray platform. We found that although the amplification step increased the sensitivity of detection of miRNAs, we observed a concomitant decrease in specificity for closely related probes, as well as increased variation introduced by dye bias. CONCLUSION: The data presented in this investigation identifies several important sources of systematic bias that must be considered upon linear amplification of miRNA for microarray analysis in comparison to directly labelled miRNA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".